Categories

Quantitative and Qualitative Designs

Quantitative and qualitative designs share one common feature: To be credible, they must be rigorously addressed using best practice in scholarly research design. Qualitative designs employ methods to assess risk based on non-numerical categories or levels. Quantitative designs assess risk based using numeric data (ratio and interval) where the meanings and proportionality of values are maintained inside and outside the context of the assessment. Semi-quantitative designs assess risk using nominal, ordinal, interval, and ratio levels of measurement; as such the context semi-quantitative designs is extremely important. An important factor to consider is that in semi-quantitative designs values and meanings may not be maintained in other contexts.

It is well known that senior leaders and policy makers prefer risk quantification to qualitative assessments.[i] This preference is a very important factor to keep in mind when designing a risk study plan or choosing a risk methodology. In practice, semi-quantitative methods are often used to overcome data limitations and address all the risk decision issues.[ii] In general, the outputs of the assessment and the decision to be informed should determine if the data requirements, data generation, and collection plan. In practice, the vast majority of risk assessments are semi-quantitative. This is because numeric data is rarely sufficient to cover the landscape of the risk assessment. As a result, human judgment and other qualitative inputs are organized into scales and bins such as Sherman Kent Scales [1] or indexes. The table below is helpful in determining when to use quantitative and qualitative approaches [2].